Evolution of the driving styles of anticipatory agent remotely operating a scaled model of racing car

We present an approach for automated evolutionary design of driving agent, able to remotely operate a scale model of racing car running in a fastest possible way. The agent´s actions are conveyed to the car via standard radio control transmitter. The agent perceives the environment from a live video feedback of an overhead camera. In order to cope with the inherent video feed latency, which renders even the straightforward tasks of following simple routes unsolvable, we implement an anticipatory modeling - the agent considers its current actions based on anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. The driving style (i.e. the driving line combined with the speed at which the car travels along this line) is first evolved offline on a software simulator of the car and then adapted online to the real world. Experimental results demonstrate that on long runs the agent-operated car is only marginally slower than a human-operated one, while the consistence of lap times posted by the evolved driving style of the agent is better than that of a human. This work can be viewed as a step towards the development of a framework for automated design of the controllers of remotely operated vehicles capable to find an optimal solution to various tasks in different traffic situations and road conditions.